Regression With Scikit Learn (Part 1)
This is Day 29 of the #100DaysOfPython challenge.
This post will look into how we can create a linear regressor to make predictions about continuous target variables.
Source code can be found on my GitHub repo
- Familiarity Conda package, dependency and virtual environment manager. A handy additional reference for Conda is the blog post "The Definitive Guide to Conda Environments" on "Towards Data Science".
- Familiarity with JupyterLab. See here for my post on JupyterLab.
- These projects will also run Python notebooks on VSCode with the Jupyter Notebooks extension. If you do not use VSCode, it is expected that you know how to run notebooks (or alter the method for what works best for you).
Let's create the
regression-with-scikit-learn directory and install the required packages.
# Clone and add a Python notebook $ git clone okeeffed/supervised-learning-with-scikit-learn-template regression-with-scikit-learn $ cd regression-with-scikit-learn $ touch docs/regression.ipynb
At this stage, we are ready to add in a linear regressor.
Exploring the Boston dataset
In this example, we will use the Boston housing dataset to predict the price of a house.
In our file
docs/regression.ipynb, we can add the following:
from sklearn.datasets import load_boston boston = load_boston() print(boston.DESCR)
The above will print out the dataset description.
:Number of Attributes:, we see the description "13 numeric/categorical predictive. Median Value (attribute 14) is usually the target."
It is the
MEDV that we will be trying to predict.
In our second cell, add the following:
X = boston.data y = boston.target # Create the dataframe import pandas as pd df = pd.DataFrame(X, columns=boston.feature_names) print(df.head()) # print the first 5 rows
Here we are assigning the
y variables to the
boston dataset based on features and target respectively.
From there, we are creating a data frame with the features and target with panda.
Printing the head shows us the first five rows:
CRIM ZN INDUS CHAS NOX RM AGE DIS RAD TAX \ 0 0.00632 18.0 2.31 0.0 0.538 6.575 65.2 4.0900 1.0 296.0 1 0.02731 0.0 7.07 0.0 0.469 6.421 78.9 4.9671 2.0 242.0 2 0.02729 0.0 7.07 0.0 0.469 7.185 61.1 4.9671 2.0 242.0 3 0.03237 0.0 2.18 0.0 0.458 6.998 45.8 6.0622 3.0 222.0 4 0.06905 0.0 2.18 0.0 0.458 7.147 54.2 6.0622 3.0 222.0 PTRATIO B LSTAT 0 15.3 396.90 4.98 1 17.8 396.90 9.14 2 17.8 392.83 4.03 3 18.7 394.63 2.94 4 18.7 396.90 5.33
This information can give us some insight to what the data will look like.
We want to create a linear regressor to predict the
MEDV variable based on the number of rooms, so we will need to adjust our X to only pass the one dimension.
X_rooms = X[:, 5] # We need to reshape our data from a 1d to a 2d array # For more info, see https://jakevdp.github.io/PythonDataScienceHandbook/02.02-the-basics-of-numpy-arrays.html#Reshaping-of-Arrays # Also see https://numpy.org/doc/stable/reference/generated/numpy.reshape.html X_rooms = X_rooms.reshape(-1, 1) df = pd.DataFrame(X_rooms, columns=[boston.feature_names]) print(df.head()) # print the first 5 rows
The above code will only take the data for the
rooms feature and reshape it to a 2d array.
The resulting data frame is the following:
RM 0 6.575 1 6.421 2 7.185 3 6.998 4 7.147
Visualizing the data
We can take the variables we have assign
y to visualize the data.
In a new cell, add the following:
import matplotlib.pyplot as plt plt.scatter(X_rooms, y) plt.ylabel('Value of house /1000 ($)') plt.xlabel('Number of rooms') plt.show()
Executing that code gives us the following:
Value of house /1000 ($) vs Number of rooms
As you could imagine intuitively, the price of the house rises as the number of rooms increase.
Creating a regressor to predict a continuous target variable
Finally, we can build a linear regressor to predict the
import numpy as np from sklearn.linear_model import LinearRegression reg = LinearRegression() # Fit the regressor to the data reg.fit(X_rooms, y) prediction_space = np.linspace(min(X_rooms), max(X_rooms)).reshape(-1, 1) # Re-create the scatter plot plt.scatter(X_rooms, y) plt.ylabel('Value of house /1000 ($)') plt.xlabel('Number of rooms') # We add the prediction to the plot as a black line plt.plot(prediction_space, reg.predict(prediction_space), color='black', linewidth=3) plt.show()
This provides us with a visual line of the predicted values on the linear regressor.
Adding the line to the data
Today's post was an introduction to regression with Scikit Learn. We used the Boston dataset to predict the
Moving forward, we will dive deeper into linear regression theory apply this to a test/train split. Then we will look into cross-validation, as well a regularization.
Resources and further reading
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